Background
RS Industria is a cloud-based platform for condition monitoring and predictive maintenance, designed to collect real-time data from manufacturing machines. Its goal is to help manufacturers reduce costs, whether by lowering energy usage, minimising machinery downtime, or cutting waste.
Customers rely on three core features of the platform:
Rule-Based Alerting: Allows users to set specific conditions and receive alerts via email or SMS when these conditions are triggered, enabling quick responses to potential machine issues.
Data Studio: A powerful tool for analysing asset usage, cost data, and production data, helping users identify trends and make informed decisions.
Dashboards: A flexible way to display multiple visualisations, perfect for regularly comparing data, especially on large screens within factory settings.
RS Industria was evolving, we had plans to build new capabilities in the platform that included production data and custom formulas.
The data studio page is a key feature as this is the playground for customers where they are able to add data and manipulate it to their choosing. However in its current state it was not possible for it to handle these new features and was blocking us from delivering them.
My Role
As the Design Lead, I worked closely with the Technical Product Owner, Digital Product Director, and key stakeholders across multiple markets to deliver an effective solution. My core responsibilities included:
Discovery: Mapped the existing customer journey to identify pain points and opportunities for improvement.
Design: Created a prototype to address identified customer problems, ensuring the design effectively solved their challenges and aligned with user needs.
User Testing: Conducted unmoderated testing with customers from key markets to validate prototypes and gather insights.
Development Support: Collaborated with the development team to write and refine user stories, while assisting QA during testing.
Phase 1: Refactoring Data Studio
Discovery
We conducted interviews with key users, including Solution Engineers and customers, to understand their pain points and gather feedback.
Design
In collaboration with solution architects and the development team, we worked together to define the project scope. The focus of the first iteration was primarily focussed on improving back-end processes, allowing us to deliver value to the customer more quickly. I supported this phased approach, as it helped avoid a "big bang" rollout that could have disrupted established user patterns and potentially led to confusion. Taking the findings from the discovery sessions I proposed the following changes.
Testing
We tested the designs with Solution Engineers and customers, and the feedback was overwhelmingly positive. Users appreciated the improved clarity, particularly the ease of managing parameters.
One suggestion emerged during testing when a customer mentioned that they often selected multiple parameters for an asset but didn’t always want to add them immediately. The proposed process (clicking "Add to Chart," finding the asset, and then selecting parameters) felt tedious.
To address this, we proposed adding an "Add Parameters" button next to the asset name. This would open the parameter selection modal with the asset already pre-selected, simplifying the process.
Phase 2: Adding Production Data
Customers had highlighted to us that they were manually calculating and reporting the ratio of production to energy for daily meetings. They complained that this was time consuming and error prone and limited their time for for deeper analysis in identifying opportunities for improvement. This ratio is a lead indicator to their performance and having an easy way to do this in RS Industria would add a lot of value to them.
Discovery
To better understand customer needs regarding production data, I conducted several user interviews. These discussions revealed key insights into how customers expect to use and manage production data:
Data Flexibility
Customers wanted the ability to customise how they capture production data based on their specific operations:
Some wanted data categorised by the products they produce.
Others preferred to capture it based on factory lines or product categories.
Some customers wanted data organised by job.
Current Processes
The methods customers currently used to gather production data varied widely:
Some manually entered the data into documents.
Others received it from different departments within their organisation.
Some retrieved data from external systems.
Time Periods of Interest
Customers primarily focused on daily and weekly totals for production data. While there was some interest in hourly totals, it was noted that this would be difficult to manage without integration with an automated system.
Additional Data
In addition to capturing production totals, customers expressed the need to also track product rejects.
Design
I collaborated closely with the Solutions Architect and the development team to design a solution that met customer needs. And proposed the following flow:
Testing
After finalising the designs, we conducted user testing with customers. The feedback was positive, with all participants successfully completing the assigned tasks without any difficulties.
Key findings included:
All users were able to create a production volume, enter data, and view the production data in the chart.
Customers found the interface intuitive and appreciated the flexibility in how they could input and view their production data.
Phase 3: Adding Formulas
While integrating production data and visualising it was a significant achievement, customers still needed to manually calculate the ratio of energy usage to factory output. to get this value they still had to manually do this which could leads to errors and inconsistencies. This ratio calculation would would make it easier to identify inefficiencies, allowing them to make informed decisions on optimising energy usage and improving overall efficiency.
Discovery
I had a series of conversations with our Solution Engineers who I felt were best placed to highlight the benefits of the formulas and how they could be used considering in their role they handled the installation and helped customers identify issues with assets using the platform.
We also knew that for this first phase of formula calculations would use basic BODMAS (Brackets, Orders, Division, Multiplication, Addition, Subtraction) and I was keen to understand how they saw this formula creation being used. From these discussions they were able to highlight to me the following use cases:
Measuring Asset Efficiency
Compare the amount it's produced against the energy the asset has used. The formula for this would look like the following:
Bread KG / Oven Total kWh
Unit Conversion
Convert gas meter from m3 to kWh. The formula for this would look like the following:
Gas Meter Total m3 * 1.02264
Percentage of Total Energy Usage
To see the percent of the total factory energy used by a group of assets. The formula for this would look like the following:
((Oven 1 Total kWh + Oven 2 Total kWh) / Electric Meter Total kWh) * 100
Design
During the design phase for this I wanted to
Allow customer to enter different data types - asset data, production data and static values
Colour coded the data types and operations
Customer would be able to type in the formula box like any other text input
Handover and Implementation
To ensure a smooth handover, I documented detailed user stories and collaborated with the development team to refine them. This process included answering their questions and providing clear specifications for the new components and design tokens.
Throughout the sprints, I maintained close communication with the development team, offering ongoing support. I addressed questions as they arose and identified areas where components or design tokens were incorrectly implemented, ensuring alignment with the design vision.
Outcome
The enhancements to RS Industria’s Data Studio have given customers a more intuitive and powerful tool for understanding their factory performance. We have received positive feedback from customer detailing how it's much easier and we've already seen examples of it helping customers identify areas for improving the efficiency of their sites.